Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture

SO2 presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal–organic frameworks (MOFs), cost-effective separation processes for SO2 emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO2/CH4, SO2/CO2, and SO2/N2 separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO2/CH4 selectivities of 62.4–16899.7, SO2 working capacities of 0.3–20.1 mol/kg, and SO2 regenerabilities of 5.8–98.5%. Regarding SO2/CO2 separation, they possess SO2/CO2 selectivities of 13.3–367.2, SO2 working capacities of 0.1–17.7 mol/kg, and SO2 regenerabilities of 1.9–98.2%. For the SO2/N2 separation, their SO2/N2 selectivities, SO2 working capacities, and SO2 regenerabilities span the ranges of 137.9–67,338.9, 0.4–20.6 mol/kg, and 7.0–98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.


INTRODUCTION
20th and 21st centuries have witnessed a significant expansion of the industrialization across the globe. Over time, it has been better understood that chemical processes should be run such that the environmental sustainability can be preserved, otherwise, the current global challenges such as air pollution can deteriorate in the future. Currently, the capture of toxic gases from the industrial gas streams and open air is highly critical as the release of acidic gases like SO 2 into air can lead to acid rain and/or smog, damaging both the human health and environment. 1,2 It has been reported that in 2018, 62.7 Mt of SO 2 was emitted to the atmosphere, demonstrating the large extent of the SO 2 emission problem despite the techniques being used to mitigate the emissions. 3 Traditionally, the gas separations in the industry are conducted through energy-intensive processes like cryogenic distillation and absorption. 4 In comparison, adsorption processes can achieve gas separations much more efficiently by taking advantage of sorbate−sorbent interactions around room temperature. 5 For adsorption processes, there are multiple classes of porous materials that can be implemented. Of them, metal−organic frameworks (MOFs) are highly promising as they are ordered and chemically diverse materials that have been widely investigated especially after 1990s. 6,7 As their pores can be tailored in terms of size, shape, and functionality, MOFs can offer exceptional gas separation performances with disparate affinities for different adsorbates. 8,9 In the recent years, more studies started to emerge about the SO 2 adsorption/separation as a part of efforts to tackle toxic gas problems. As an air pollutant, SO 2 co-exists with other gases like CH 4 , CO 2 , and N 2 in the atmosphere 10,11 where the relative ratios of SO 2 over other gases can differ considerably depending on the region and operation conditions of the power and/or industrial plants in the region. 12,13 There are many separation studies where SO 2 concentrations in binary mixtures vary in a large range of 0.2 to 90%. 3,14−29 For instance, Zhang et al. 19 experimentally tested the SO 2 uptake and separation capability of a Cu-based MOF, CPL-1, under ambient conditions and reported a SO 2 saturation capacity of 44.8 cm 3 /g, SO 2 /CH 4 ideal selectivity of 74.3, SO 2 /N 2 ideal selectivity of 368, and SO 2 /CO 2 ideal adsorbed solution theory (IAST) 30 selectivity of 8.7. In a similar experimental work by Zhang et al., 18 ELM-12 is reported to have SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 IAST selectivities of 871, 30, and 4064 under ambient conditions for 10% SO 2 involving mixtures. Considering SO 2 /CO 2 separation, Brandt et al. 24 demonstrated that MIL-160 can attain IAST selectivity of ∼125 for a SO 2 /CO 2 (50/50) mixture around ambient conditions. Similarly, Zhu et al. 25 estimated the IAST selectivity of MOF-808-His as 90.5 for a SO 2 /CO 2 (10/90) mixture under ambient conditions. Zaŕate et al. 31 synthesized an Al-based MOF (CAU-10) for which experimental (simulated) SO 2 uptake under ambient conditions is determined to be ∼4.5 (5.2) mol/kg. In another work by Zaŕate et al., 22 MFM-300(Sc) is reported to have an experimental SO 2 uptake of 9.4 mol/kg under ambient conditions, which agrees with the simulated uptake. Glomb et al. 32 synthesized an interpenetrated Zn-based MOF functionalized with urea, exhibiting a large SO 2 uptake of 10.9 mol/kg around ambient conditions.
The expansion of the computational resources and more efficient algorithms have enabled screening the adsorption/ separation properties of many materials by which guidance could be provided for the future experimental efforts saving significant time and cost. For instance, Sun et al. 33 studied 12 porous materials using molecular simulations for the SO 2 capture from a flue gas mixture and concluded that Cu-BTC and MIL-47 are the best-performing MOFs at 313 K, up to 1 bar in terms of selectivity. Zhang et al. 20 computationally studied SO 2 capture from SO 2 /CO 2 and SO 2 /N 2 mixtures using porous aromatic frameworks (PAFs) where it has been concluded that the incorporation of the functional groups (−CH 3 , −CN, −COOH, −COOCH 3 , −OH, −OCH 3 , −NH 2 , and −NO 2 ) into PAF-1 boosts the SO 2 selective behavior of the materials especially below 10 bar. Maurya and Singh 34 simulated the SO 2 adsorption in several adsorbents (COF-108, COF-300, singlewalled carbon nanotube (SWCNT), InOF-1, UiO-66, and ZIF-8) around ambient conditions where the SO 2 uptakes of UiO-66 and ZIF-8 (∼5 mol/kg) are found to be several folds lower than that of SWCNT (∼23 mol/kg). Li et al. 9 performed grand canonical Monte Carlo (GCMC) simulations to investigate the SO 2 capture from SO 2 /CO 2 and SO 2 /N 2 mixtures using UiO-66 and its functionalized variants where it has been concluded that UiO-66-(COOH) 2 and UiO-66-COOH exhibit two of the highest adsorption selectivities at low pressures (SO 2 /CO 2 and SO 2 /N 2 selectivity higher than ∼40 and 3000, respectively). As expected, the computational studies focus on higher number of materials than the experimental studies, despite investigating only tens of materials at maximum.
While the sheer number of MOFs implies bigger opportunities, it also necessitates the use of computational tools to expedite the identification of the potentially useful materials. Indeed, it has been previously shown that the computational screening efforts can guide the experimentalists toward the right direction and realize high-performing materials in the laboratory. 35,36 Motivated by this, a multi-level computational screening study is presented in this work to eventually unlock SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 separation performances of 1770, 2255, and 1909 different MOF materials (filtered from more than 300,000 MOFs), respectively, which constitutes the largest scale computational screening for SO 2 capture, to the best of our knowledge. In this work, we chose the concentration of SO 2 in binary mixtures as 10%, which enables the comparison of SO 2 separation performances of MOFs studied herein with potentially high-performing porous materials probed in many studies 3,[16][17][18][19]22,[24][25][26]28 where binary SO 2 /CH 4 , SO 2 /CO 2 , and/or SO 2 /N 2 mixtures involve 10% SO 2 . While SO 2 separation from ternary or quaternary mixtures would also be an interesting topic, it is beyond the scope of our work. We first investigate the separation performances of bare hypothetical MOFs and then explore functionalized variants of the top 50 bare materials. Comparing the performances of functionalized and bare MOFs, the most beneficial functional groups are identified for each separation in addition to examining the structure−performance correlations. So far, many studies 37−40 showed that addition of functional groups improves the separation performances of bare MOFs. Our work demonstrates not only the advantages but also disadvantages of functional group addition using one of the largest functionalized MOF sets.

COMPUTATIONAL METHODS
The separation of SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 mixtures (10% SO 2 content in each) is studied using a hypothetical MOF database 41 comprising more than 300,000 MOFs. The structures of the database are named as mX_oY_tpl.f where mX (oY) denotes a specific metal (organic) building unit, tpl designates the structure topology, and f represents an internally coded functional group. Thus, a certain f number may not necessarily represent the same functional group in different structures. The porous networks of the structures are analyzed using Zeo++ 42,43 with a probe radius of 1.84 Å to determine global cavity diameter (GCD), pore limiting diameter (PLD), largest cavity diameter (LCD), surface area, probe-occupiable void fraction, and pore volume. All structures investigated in this work were publicly made available on https://archive. materialscloud.org/record/2018.0016/v3. Mixture gas adsorptions are calculated using GCMC simulations in RASPA. 44 The GCMC simulations are conducted at two levels where the first one involves the hypothetical MOFs for which no functional group is mentioned (we will refer those materials as bare MOFs), while the second one encompasses both the top 50 bare MOFs and their functionalized variants (those reported with "H" functional group in the database are indeed bare MOFs. Thus, while breaking down the structures into functional groups at the second level, they are collected into "bare" group). The inaccessible pores for sorbates were identified using spheres that are slightly smaller (0.2 Å) than the corresponding sizes of sorbates and blocked via Zeo++ at the first level of the screening. At both levels of GCMC simulations, MOFs whose PLDs are less than sorbate sizes and those with no accessible surface area were excluded. Only structures having no open metal site were investigated (open metal site identification was carried out using Zeo++). In the GCMC simulations, the following moves were allowed with equal probabilities: insertion/deletion, translation, rotation (excluding CH 4 ), and identity change. Simulations to determine the gas uptakes were performed at 298 K, 1 (adsorption pressure), and 0.1 (desorption pressure) bar where 20,000 simulation cycles are equally split into equilibration and production cycles. The adsorbate density profiles and radial distribution functions (RDFs) were obtained using 20,000 and 60,000 simulation cycles, respectively, with equal equilibration and production cycles. The interactions of MOF atoms with the gas molecules were defined by universal force field (UFF) 45 parameters and partial atomic charges in MOFs (PACMOF). 46 PACMOF charges of hypothetical MOFs were determined using a machine-learning model fitted to density-derived electrostatic and chemical (DDEC) charges (based on 2017 version of the Chargemol package). 4748−54 The sorbate interaction parameters were acquired from earlier studies. 55−57 The truncation distance for Lennard-Jones interactions was 12 Å. Electrostatic calculations were calculated using the Ewald summation method. 58 Structures were kept rigid throughout the simulations.
The adsorption selectivity is expressed as S N N y y ads,1/2 which N represents the adsorbed gas amount obtained from GCMC simulations and y is the mole fraction of the gas component in the bulk mixture. Working capacity of a sorbate in a structure is essentially the difference between gas uptakes at the adsorption and desorption conditions (ΔN 1 = N ads,1 − N des,1 ). Regenerability of a structure is defined as R (%) 100 The materials were ranked by the individual gas separation performances (i.e., adsorption selectivity, working capacity, and regenerability), and the overall rankings of materials were determined using the summations of the individual separation performance-based rankings. Thus, the top-ranked materials have the highest overall rankings. We also ranked the materials based on the separation potential (ΔQ), 59 which was calculated as represents the volumetric uptake capacity of SO 2 . While SO 2 may co-exist with H 2 O, it is known that H 2 O adsorption simulations in porous media are typically computationally expensive. 60 Also, it has been reported that humid SO 2 exposure can degrade the MOFs considerably while the same MOFs can remain stable after dry SO 2 exposure. 61,62 This implies that while GCMC simulations for humid mixtures could have been performed, GCMC results might not describe the gas adsorption/separation behavior of MOFs accurately as the degradation of MOFs is not accounted for in simulations employing rigid frameworks. To eliminate such complexities and keep computational cost at a reasonable level, we simulated dry gas mixtures. To reveal the water affinities of the top structures that we identified, Henry's constant (K H ) and enthalpy of adsorption (−ΔH) for H 2 O (TIP4P model 63 ) were calculated at infinite dilution using at least 1,000,000 Widom insertions at 298 K. Adsorbate density profile images were obtained via Paraview. 64

RESULTS AND DISCUSSION
In our work, we intended to identify the best-performing bare MOFs as the starting platform and probed the functionalized variants thereof to understand which functional group(s) can improve already good performances of bare MOFs. The idea stems from the fact that, in general, it is harder to obtain improvements in separation performances of materials that already perform well. A joint experimental computational work involving about 2 orders of magnitude less number of materials has recently been published. 65 Therefore, our work focuses on the elucidation of the potential improvement/deterioration in the separation performance of materials due to the grafting of the functional groups on high-performing bare MOFs but not determining separation performances of the entire >300,000 hypothetical MOFs which would be too costly.
Specifically, for all three separations, in the first level of the screening, MOFs, for which no functional group is reported, are filtered from the aforementioned hypothetical MOF database and employed in the GCMC simulations. Using gas separation performance metrics obtained from GCMC simulations, the top performing materials are identified based on the overall rankings, as described above. In the second level of the screening, the top 50 bare MOFs identified in the preceding level and their functionalized variants are utilized in the GCMC simulations from which the top performing hypothetical MOFs are ascertained.
Before we discuss the simulation results, we would like to comment on the choice of UFF. As shown in Table S1, the experimental and simulated SO 2 uptakes at 1 bar, 298 K in MFM-300(In), and SIFSIX-1-Cu show good agreement (8.28 vs 7.79 mol/kg and 11.01 vs 11.85 mol/kg, respectively). The comparisons of experimental and simulated gas uptakes were based on excess gas uptake values. Helium void fractions, to obtain excess gas uptakes from absolute gas uptakes, were obtained using 10,000,000 Widom insertions at 298 K using the parameters reported earlier. 66 Having good agreement between experimental and simulated gas uptakes across a certain number of materials would not necessarily guarantee that all simulated gas uptakes would be accurate. This could be due to imperfect experimental crystals (e.g., presence of defects), reproducibility challenges for experimental gas uptakes even in the same material, deficiency of force fields, and/or charge partitioning methods. 67−69 The main reason to use UFF in the screening studies is that it can predict similar rankings of materials with respect to those obtained by ab initio force fields or experiments, as shown earlier for CO 2 adsorption, CO 2 /H 2 selectivity, Xe adsorption, Kr adsorption, and Xe/Kr selectivity. 70−73 Thus, we employed UFF in this large-scale screening study to obtain trends (e.g., material rankings), and shortlists of promising materials which are more likely to perform better than others.
3.1. SO 2 /CH 4 Separation. Figure S1 illustrates the SO 2 / CH 4 separation performances of 1295 bare hypothetical MOFs together with their pore features. The top left panel demonstrates that the SO 2 /CH 4 selectivity, SO 2 working capacity, and SO 2 regenerability span the ranges of 3.2− 5773.0, 0.1−20.7 mol/kg, and 7.9−98.9%, respectively. While most of the pristine MOFs (992 MOFs) are highly SO 2 regenerable (>80%), the 10 most SO 2 selective (over CH 4 ) MOFs exhibit low SO 2 regenerability (8.0−25.2%). The most selective MOFs (selectivity >2000) are those with very narrow pore sizes (4.51−6.88 Å), bringing about significant confinement effects. However, as the narrow pore sizes cause strong interaction potential overlaps at both adsorption and desorption pressures, these structures demonstrate low working capacity and regenerability. Those with the largest SO 2 working capacities (17.6−20.7 mol/kg) are also largely SO 2 regenerable (86.7−98.8%), whereas in the low SO 2 working capacity range (<5 mol/kg), SO 2 regenerabilities vary in a broad range (7.9− 96.2%). The large SO 2 working capacities are associated with the large-pored structures, in which SO 2 adsorption is relatively weak at the desorption pressure, leading to not only high working capacity but also high regenerability. In the low working capacity range, SO 2 selectivities span the entire spectrum (3.2− 5773.0), suggesting that significant trade-offs can be seen across selectivity and working capacity. There is a branch in the plot (selectivity < 20 and working capacity < 5 mol/kg) where selectivities drop with the decrease in the working capacity. This region involves structures with highly varying PLDs (6.59− 21.89 Å) where the SO 2 uptakes at the adsorption pressure span a narrow range of 0.1−1.1 mol/kg, accompanied with a narrow SO 2 regenerability range (85.9−92.8%).
The top right panel relates the SO 2 /CH 4 selectivity and SO 2 working capacity with the porosity (i.e., void fraction) of the structures. Not surprisingly, large SO 2 working capacities (>10 mol/kg) are predicted for some of the vastly porous structures (void fraction >0.7); however, those with the highest void fractions (0.881−0.919) are found to attain very limited SO 2 working capacities (<2 mol/kg), implying that there is not a straightforward relation between SO 2 working capacity and void fraction. The selectivity versus PLD relation in the bottom left panel demonstrates that the most SO 2 selective (over CH 4 ) structures are those having narrow pore sizes. However, around small PLD values (5−6 Å), SO 2 /CH 4 selectivities extend in a large range (21.3−5773.0), implying that a material screening solely based on PLD values would not result in a shortlist of materials with only high selectivities. Similarly, especially for small PLDs (<6 Å), the void fractions can vary greatly (0.166− 0.744), signifying the diverse structural properties of the structures. As the pore sizes expand, structures lose their SO 2 selective (over CH 4 ) behavior significantly with the lowest SO 2 / CH 4 selectivity of 3.2 at a PLD of 13.30 Å. The bottom right panel depicts that the most SO 2 selective (over CH 4 ) structures are populated in a wide surface area spectrum (1325.4−3360.5 m 2 /g), which resembles a peak as selectivities are lower at smaller and larger surface area values. However, it is also seen that there are many other MOFs with similar surface area values with significantly less selective behavior.   4 and 2531.4 m 2 /g, while those next to them have lower SO 2 /CH 4 selectivities giving rise to a peak around 2000 m 2 /g. It also portrays the large extents of selectivities (89.9−16,792.9) in a narrow surface area range (1600−1800 m 2 /g), which is not surprising as selectivities are governed by multiple factors such as pore size, shape, porosity, functional group, and so forth.       Figure S3 illustrates the SO 2 /CO 2 separation performance metrics and textural properties of the top 50 performing bare MOFs and their functionalized variants. As the top left panel shows, the most SO 2 selective MOFs have very limited working capacities and regenerabilities. In general, MOFs having high SO 2 working capacities also possess large regenerabilities, as exemplified by m2_o12_o27_pcu.138 having the largest SO 2 working capacity of 17.7 mol/kg and a high SO 2 regenerability of 96.5%. The top right panel shows that going from low to high SO 2 working capacities, void fractions generally increase. This implies that the SO 2 adsorption at the desorption pressure in MOFs with high void fractions remains at relatively low values while that at the adsorption pressure can attain much larger values than those in MOFs with limited void fractions despite some exceptions. Considering the SO 2 /CO 2 selectivity, PLD, and void fraction correlations in the bottom left panel, it can be deduced that at a particular PLD value, widely varying SO 2 /CO 2 selectivities and void fractions can be obtained. The bottom right panel shows that the most SO 2 selective MOFs are not the ones with very low or high surface areas but instead moderate surface areas. Bare MOFs stand in the middle of the boxplot in terms of mean SO 2 /CO 2 selectivities, suggesting that depending on the type of functional groups, MOF functionalization can lead to higher or lower mean SO 2 /CO 2 selectivities. Some MOFs with multiple functional groups attain the lowest SO 2 /CO 2 selectivities (e.g., m3_o13_o24_pcu.240 functionalized with −Br and −HCO groups having the smallest SO 2 /CO 2 selectivity of 13.3), indicating that multiple functional groups can be less beneficial for selectivity than single type of functional groups. The top right panel demonstrates that bare MOFs tend to show higher SO 2 working capacities than the functionalized MOFs. Among the functionalized MOFs, those with −Me, −F, and −OH groups attain the largest SO 2 working capacities on average. MOFs with multiple functional groups tend to show the lowest SO 2 working capacities, as evidenced by their smallest mean and median SO 2 working capacities. However, they also exhibit one of the largest spreads in SO 2 working capacity (14.6 mol/kg), in which the smallest (largest) SO 2 working capacity of 0.1 (14.7) mol/kg is attained by a MOF functionalized with −Br and −HCO (−Cl and −F) groups. All in all, these distributions hint that adjusting the gas affinities of MOFs through functionalization may not necessarily lead to higher SO 2 working capacities than those of bare MOFs as the pore spaces typically decrease via functionalization, and the gas uptake could be strong not only at the adsorption pressure but also at desorption pressure.
The middle-left panel depicts that the MOFs with three different halogen (−F, −Cl, and −Br) groups and bare MOFs demonstrate the largest SO 2 regenerabilities with comparable mean SO 2 regenerabilities (around 93−95%) and small spreads.         Figure 5 shows the adsorbate density profiles obtained from GCMC simulations at 0.1 bar, 298 K in the top three materials for SO 2 /CH 4 separation in which SO 2 molecules are relatively more localized near the pores, whereas CH 4 molecules are typically dispersed throughout the material. Examining the SO 2 and CO 2 density profiles in the top three materials in Figure S6, it can be inferred that SO 2 molecules prefer the pore corners while the distribution of weaker adsorbing sorbate, CO 2 , can be narrower (compared to CH 4 in the SO 2 /CH 4 mixture). For the SO 2 /N 2 separation, the density profiles in Figure S7 are akin to those for the SO 2 /CH 4 separation where the relatively small quadrupole moment of N 2 does not lead to localized N 2 regions. The formation of SO 2 clusters, which was attributed to the strong host−guest and dipole−dipole interactions between SO 2 molecules, 15,16,26,74 near the pore walls in all three gas separations are in line with the observations made for IRMOF-10, MFM-300(Al), MFM-601, M(bdc)(ted) 0.5 (M = Ni, Zn), and SIFSIX materials. 15,26,74−76 As RDFs obtained at 0.1 bar, 298 K demonstrate (Figures S8−S10) that SO 2 molecules are typically located close to the O atoms of the framework where S SO 2 ···O host interactions stabilize the sorbates as the interactions between SO 2 and O of furan linker in MIL-160 do. 24 Since the SO 2 molecules are about 3−4 Å away from H atoms of the framework, they can interact through hydrogen bonds as in NOTT-300, MFM-300(In), and SIFSIX materials, where interactions between O of sorbate and H of the framework contribute to the sorption. 26,28,77 Similarly, in some of the top MOFs involving N or Cl atoms, SO 2 molecules are at relatively close distances, which enables strong electrostatic interaction between S atom of the sorbate and N or Cl atoms of the framework. A similar observation has been made for MOF-808-His where SO 2 interacts favorably with N atoms of the framework. 25 While strong adsorption has been seen in the top materials, as reported earlier, some MOFs may be deprived of strong SO 2 interaction sites such as MOF-808 whose gas uptake is limited. 25 One strategy to render such MOFs efficient for SO 2 capture is functionalization, as exemplified by MOF-808-His, underlining the importance of investigating the functionalized MOFs whose bare forms may not show significant gas uptake or separation capability. 25 Through functionalization, the pores may provide stronger host−guest interactions as they can be narrower (leading to stronger confinement effects) or grafted favorable interaction sites for sorbates.
Having discussed the separation performances of hypothetical MOFs of interest, now, we advance to the performance benchmarks of them with highly SO 2 selective materials reported in the literature. Liu et al. 14 performed GCMC simulations for the SO 2 /CO 2 separation in MIL-160 and MFM-300(Al) where their selectivities are predicted to reach 220 and 53, respectively, under ambient conditions. Savage et al. 28 estimated the SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 selectivities of MFM-300(In) under ambient conditions as 425, 60, and 5000, successively. Cui et al. 26 reported the SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 selectivity of SIFSIX materials using IAST and showed  Considering the well-defined structures of MFM-300(In) and SIFSIX-1-Cu and good agreement between the experimental and simulated SO 2 uptakes in MFM-300(In) and SIFSIX-1-Cu (see Table S1), we calculated their SO 2 gas separation performances (at the conditions specified in the Computational Methods section) as well to demonstrate the relative performances of hypothetical MOFs with respect to the synthesized MOFs. The structures of MFM-300(In) and SIFSIX-1-Cu were obtained from the literature. 28,78 MFM-300(In) and SIFSIX-1-Cu were reported to have high SO 2 /CH 4 (425 and 1241.4), SO 2 /CO 2 (60 and 70.7), and SO 2 /N 2 (5000 and 3145.7) selectivities calculated using IAST for binary mixtures involving 50 and 10% SO 2 under ambient conditions, respectively. 26,28 For all three gas separations, MFM-300(In) demonstrates highly SO 2 selective behavior (SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 selectivities of 3128.5, 181.1, and 24,937.1, respectively); however, its SO 2 working capacity (0.6, 0.4, and 0.6 mol/kg, respectively) and regenerability (7.5, 6.1, and 8.2%, respectively) are low. In contrast, SIFSIX-1-Cu has somewhat less selectivity (SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 selectivities of 803.7, 46.8, and 4423.7, respectively) but possess much higher SO 2 working capacities (8.8, 6.8, and 8.9 mol/kg, respectively) and regenerabilities (82.4, 73.7, and 82.5%, respectively). Comparing these with the separation performance metric ranges of hypothetical MOFs, it can be inferred that there are many hypothetical MOFs that can be superior to MFM-300(In) and SIFSIX-1-Cu.
The separation performances of MOFs were also assessed using ΔQ, correlated with productivity, and a considerably different ranking of materials (compared to that obtained using adsorption selectivity, working capacity, and regenerability) is observed (see Table S2). While this may suggest that the identified top materials may not be the best performers at the process level, the ΔQ metric is derived using multiple assumptions (e.g., plug flow and fixed bed initially free of adsorbates), which may not be necessarily true for all separation operations. As the process-level performances rely on multiple parameters (e.g., pellet size, pellet porosity, etc.) and adsorbent bed configurations, the identified MOFs can perform well at the process level provided that the process parameters are optimized. 79,80 While hydrophobic MOFs do not necessarily meet the separation goals 81,82 and investigating water affinities of the MOFs was not one of our main goals, we calculated K H and −ΔH values for H 2 O for the top performing materials, as listed in Table 1, to find clues about their potential use under humid conditions.  82 This implies that due to the comparatively large water affinity of the top materials, H 2 O may compete with SO 2 and deteriorate the separation performances of these materials. Therefore, the materials identified for selective SO 2 removal in this work should be regarded as promising materials for dry gas mixtures but not necessarily for humid gas mixtures. However, it is worthwhile to note that separation processes are typically conducted using multiple stages. 83−85 This is because many materials, when used as single adsorbents, are not capable of performing simultaneous removal of all undesired species and/or boast higher working capacities for undesired species than those of multiple adsorbents. 83−85 Use of multiple beds also facilitates the regeneration in a continuous operation. 85 Thus, the highperforming MOFs identified in this work can still find use in the separation of gas mixtures involving SO 2 as long as H 2 O content is removed at an earlier stage in a multi-stage process. Similar processes have been previously proposed for the flue gas separation. 86−89 Also, despite not being investigated in this work, the structural stability upon gas exposure is crucial for sustainable gas separation applications. These stability tests are typically carried out under dry conditions. 61,62 In some cases while dry SO 2 exposure does not degrade materials, humid SO 2 exposure can cause the degradation, as shown for ZIF-8 and MIL-125, which underscores the importance of H 2 O removal from SO 2 involving gas mixtures. 61,62 To sum up, by breaking down the separation performance metric values into the functional groups of the hypothetical MOFs, it has been shown that the top materials for all three separations involve not only functionalized MOFs but also bare MOFs as the latter can excel at working capacity and/or regenerability, albeit not being the most selective group of MOFs. It is worthwhile to note that these gas mixtures may also have H 2 O content; 14 however, the effect of H 2 O on the separation performances of MOFs is not investigated in this study due to the high computational cost of ternary mixtures involving H 2 O. Another aspect that can emerge during SO 2 adsorption is the instability/phase transition of the MOF that may occur due to the potent interaction between SO 2 and the MOF, which are beyond the scope of this work. 3,24,61,62,74,90−92 As our study has shown that MOFs can be highly SO 2 selective, regenerable, and possess large SO 2 working capacities, these results will foster more research on the synthesis/generation/ use of MOFs for the efficient SO 2 capture from various mixtures.

CONCLUSIONS
This study focuses on the computational screening of a sheer number of hypothetical MOFs for the identification of promising candidates for SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 separations using a multi-level approach. Starting with structure filtering based on geometrical properties, a list of bare MOFs is obtained to be employed in the first level of binary GCMC simulations from which the top bare MOFs are identified. In the second level, these bare MOFs and their functionalized variants are screened to determine the materials with the best overall separation performances for the separations of interest. This screening strategy have revealed potentially high-performing hypothetical MOFs for the separation of SO 2 /CH 4 , SO 2 /CO 2 , and SO 2 /N 2 mixtures in terms of adsorption selectivity, working capacity, and/or regenerability. It is worthwhile to note that the top materials identified are those showing high (but not the highest) performances in terms of each separation performance metric implying a balanced selection of materials. Such selection criteria can help reduce the risk of shortlisting materials with unrealistically high selectivities that may arise due to the inaccuracy of the charge partitioning methods. While the entire data set screened is composed of hypothetical MOFs, we anticipate that the impressive separation performances of the top materials would trigger further experimental and theoretical